Abstract:
Driver sleepiness and weariness are substantial contributing factors to vehicular collisions. Annually, there is a consistent rise in the global count of deaths and fatalities.
This project introduces a module for an advanced driver assistance system that aims to mitigate the occurrence of accidents resulting from driver weariness, hence enhancing transportation safety. The proposed system focuses on the implementation of automatic driver drowsiness detection, utilizing visual information and Artificial Intelligence techniques.
The OpenCV algorithms under consideration demonstrate efficacy in detecting and facilitating the normalization of human facial features, but with a notable association with the majority of vehicular collision incidents.
The algorithm initiates the process by identifying facial features in color photos through the analysis of color and structural variations in both the human face and the surrounding background.
Various facial expressions and bodily movements, such as the presence of fatigue in the eyes and the act of yawning, are commonly recognized as indicators of drowsiness and exhaustion among individuals operating vehicles. These observed traits suggest that the driver’s state is suboptimal.
Driver sleepiness and weariness are frequently identified as primary contributors to vehicular accidents. Annually, there is a global increase in the fatality rate of individuals involved in such incidents.
The Driver Drowsiness Detection System allows the administrator to access the system by logging in with a designated username and password. The administrator has the ability to see a comprehensive list of all users and review their respective logs.
To access the system, users are required to complete the registration process and thereafter log in by providing a unique username and password. The Open CV framework will be employed to enable real-time detection of eye closure or yawning activities.
In the event that it detects any, the system will generate a red rectangle and append a corresponding entry to the table. Users have the ability to access and review their logs, which contain comprehensive information, using the designated feature called “My Logs.”
Note: Please discuss with our team before submitting this abstract to the college. This Abstract or Synopsis varies based on student project requirements.
Did you like this final year project?
To download this project Code with thesis report and project training... Click Here
